CCPA cookie consent & US data privacy laws

Learn what CIPA means for marketing teams striving to level up personalization.

Imagine you’re browsing an online store based in California. Until recently, the moment you landed on the site, it could track your every click, search, and purchase without asking. Why? Because the California Consumer Privacy Act 2018 (CCPA) allowed websites to collect your data by default — you were opted in until you said otherwise.

Now, picture this: You visit the same store, but instead of silent tracking, you’re greeted with a popup asking for permission to use cookies. This isn't just a courteous request — it’s the law.

California has dramatically shifted the landscape of online privacy by leveraging the California Invasion of Privacy Act 1994 (CIPA). This older law now requires websites to obtain explicit consent before loading any tracking tools or cookies. It’s a complete reversal: from opt-out to opt-in, from silent collection to upfront permission.

This change isn’t just a technicality — it’s a fundamental reimagining of how websites interact with your personal data, and it’s sending shockwaves through the digital world.

Impact on data collection and user experience

This change has several significant implications:

Data collection challenges: The potential shift to a GDPR-style, opt-in-first scenario could dramatically reduce the ability to track website visitors and communicate with third-party platforms.
Personalization hurdles: With potentially less detailed user data available, personalization efforts may take a hit. Companies will need to get creative in how they tailor user experiences with limited information.
Performance trade-offs: On the positive side, reducing third-party tags might lead to performance improvements on the front end. However, this comes at the cost of reduced analytics data, impacting the ability to deliver insights across organizations.
Legal risks: Companies can be CCPA-compliant but still face lawsuits under CIPA. It’s a stark reminder that compliance is a moving target in the privacy landscape.

5 strategies for adaptation

To navigate these changes, here are some strategies that we are considering when advising clients:

1. User-centric consent management

Develop clear, user-friendly consent forms with granular options, allowing users to choose which specific types of data they’re willing to share.

Example: A streaming service implements a layered consent form

Essential data (required): Account information, viewing history
Enhanced experience (optional):
- Recommendations based on viewing habits
- Cross-device synchronization
Marketing (optional):
- Email newsletters
- Personalized ads

Users can easily toggle each optional category on/off, with clear explanations of how their data will be used. They can update these preferences at any time through an intuitive privacy dashboard.

2. Privacy-first design

Incorporate privacy considerations into the development process from the start, not as an afterthought.

Example: A fitness app adopts a privacy-first approach:

Data minimization: Only collects essential health metrics
Local processing: Performs most data analysis on the user's device
Encrypted sync: Uses end-to-end encryption for cloud backups
Anonymous insights: Aggregates user data for research without individual identifiers
Regular audits: Conducts privacy impact assessments before adding new features

This approach is integrated from the initial planning stages, ensuring privacy is a core feature, not an add-on.

3. Alternative personalization methods

Explore techniques that don’t rely on individual tracking, such as contextual advertising or cohort-based analysis.

Example: An online sustainable products marketplace uses contextual advertising:

Page content analysis: Displays ads based on the product category being viewed
Seasonal campaigns: Adjusts promotions based on time of year, not user history
Trending items: Showcases popular products across all users, not individual preferences
Ethical values matching: Suggests products based on site-wide sustainability filters selected, not personal tracking

This approach provides relevant content without relying on individual user profiles.

4. AI and machine learning

Leverage these technologies to make the most of limited data for personalization efforts.

Example: A digital newspaper uses AI to enhance user experience with limited data:

Article clustering: Groups similar stories to suggest related content
Headline optimization: Tests multiple headlines site-wide to increase engagement
Reading time estimation: Predicts article length to help users manage their time
Topic modeling: Identifies emerging trends across all content for better categorization
Smart summaries: Generates article previews to help users decide what to read

These ML techniques improve personalization without tracking individual user behavior.

5. First-party data focus

Develop strategies to collect data directly from users through opt-in methods like surveys or account creation.

Example: A recipe website collects first-party data through user engagement:

Recipe preferences survey: Optional questionnaire about dietary restrictions and favorite cuisines
Ingredient inventory: Users can list pantry items for personalized recipe suggestions
Cooking skill assessment: Brief quiz to tailor recipe difficulty to user abilities
Recipe ratings and reviews: Encourages user feedback to improve recommendations
Optional account creation: Offers features like saved recipes and meal planning in exchange for more detailed preferences

This strategy provides value to users while ethically collecting relevant data for personalization.

Venn diagram that shows the four key takeaways of CIPA

Technical considerations

From a technical standpoint, several factors need consideration:

Tag management: A robust strategy for managing marketing technologies and data is crucial. Simply adding third-party JavaScript snippets to pages is no longer sufficient.
Central management system: Implement a system to centrally manage visitor consent and preferences, ensuring all teams are aware of and respect these choices.
Performance monitoring: Keep a close eye on page load times and front-end errors. While reducing third-party tags might improve performance, it's essential to balance this with data needs.
Data anonymization: Where possible, work with anonymized or aggregated data to minimize privacy risks.

Looking ahead

Other states will likely follow California’s lead, so the tech industry should be prepared for similar regulations to emerge elsewhere. Staying informed about evolving interpretations of CIPA and being ready to adjust strategies quickly will be key.

The goal for tech and marketing leaders should be to strike a balance between respecting user privacy and maintaining the insights needed to improve products and services. It’s undoubtedly a challenge, but with innovative thinking and a proactive approach, it’s possible to navigate these changes successfully.